cleaned up for submission

architectures/Model.py

@@ -31,13 +31,13 @@ class Model(ABC):
         optimizer = optim.Adam(filter(lambda p: p.requires_grad, self.model.parameters()), lr=rate, weight_decay=0.1)
         
         scheduler = CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-6)
-        # to save reports
-        # file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt")
-        # Util._initialize_log_file(file_path)
+        # to save training time to reports
+        file_path = Path(f"{mode}/{self.__class__.__name__.lower()}/time_metrics.txt")
+        Util._initialize_log_file(file_path)
         
 
         print(f"Starting training on {self.device}...")
-        # start_time = time.time()
+        start_time = time.time()
         # training phase
         self.model.train()
 
@@ -49,7 +49,7 @@ class Model(ABC):
                 optimizer.zero_grad()
                 # forward pass
                 outputs = self.model(inputs)
-                # comoutew loss
+                # comopute loss
                 loss = criterion(outputs, labels)
                 # backward pass
                 loss.backward()
@@ -58,9 +58,9 @@ class Model(ABC):
             scheduler.step()
         
             print(f"Epoch {epoch+1}/{epochs} | Loss: {total_loss / len(loader):.4f}")
-        #end_time = time.time()
-        #execution_time = end_time - start_time
-        #Util.log_metric(log_file=file_path, execution_time=execution_time)
+        end_time = time.time()
+        execution_time = end_time - start_time
+        Util.log_metric(log_file=file_path, execution_time=execution_time)
         if self.device.type == 'cuda': torch.cuda.synchronize()
         print(f"Training complete.")
 
@@ -123,10 +123,10 @@ class Model(ABC):
         
         print(f"Test Accuracy: {accuracy:.2f}%")
         
-        # 1. Print standard text report to terminal
-        print(classification_report(all_labels, all_preds, labels=classes, zero_division=0))
+        # Print standard text report to terminal
+        #print(classification_report(all_labels, all_preds, labels=classes, zero_division=0))
         
-        # 2. Extract structured dictionary metrics
+        # Structured dictionary metrics
         report_dict = classification_report(
             all_labels, 
             all_preds, 
@@ -135,8 +135,7 @@ class Model(ABC):
             zero_division=0
         )
         
-        # 3. Delegate file tracking to isolated helper method
-        #self._log_to_csv(mode, accuracy,report_dict)
+        # report metrics to choriographer
         return accuracy, report_dict